The driverless machine can move through rows of sorghum, going for hours with only a GPS navigation brain to guide it. High-tech cameras, meanwhile, take three-dimensional photos of every sorghum plant it passes – a kind of Google Street View for fields.

More importantly, the unmanned machine represents Iowa State University’s push into phenomics, a research frontier that promises to reshape the way we grow the food and substances we count on.

Just as genomics researchers aim to understand a plant’s total genetic makeup – every letter in the long book of its DNA – and how it influences plant development, phenomics aims to understand plant phenotypes in similar depth.

A phenotype is the way an organism grows, appears and performs, given its genetic makeup, the environment it lives in, and how the two interact.

“What’s cool – and this is what makes it most interesting to me – is the interaction between genetics and environment,” said Patrick Schnable, an ISU distinguished professor of genetics.

Schnable leads ISU’s drive into phenomics research. He took over as director of the Plant Sciences Institute (PSI) in February with the charge to bring it (and the university) to international prominence in one or more research areas.

He’s targeting phenomics, knowing the field is ripe for discovery.

Patrick Schnable (Credit: Schnable Lab)

Schnable’s manner is genial, but intense. He’s glad to talk, but there’s always a sense of urgency. Maybe it’s because besides PSI, Schnable continues his own multifaceted research effort, directs graduate students here and at Beijing’s China Agriculture University, and runs his own company, Data2Bio.

Growing up in upper New York state, Schnable read an article about famed early plant breeder Luther Burbank in Highlights for Children magazine. At the public library, Schnable checked out a Burbank biography, cementing his interest.

But while Burbank simply created new, useful plants, “what I was interested in was not the actual mechanism” of plant breeding, “but why? Why did these novel phenotypes occur?”

Schnable’s pursued those questions throughout his career. In the 2000s he helped lead the push to sequence the entire corn genome – a task even more daunting than deciphering human DNA.

While there are still many plant genomes to sequence, it’s becoming faster, easier and cheaper all the time. Similarly, scientists are finding faster ways to study how different breeds of plants and other organisms vary at the fundamental, genetic level.

With those problems in hand, researchers like Schnable are turning to the next part of the equation: figuring out how a genotype – a specific set of genes – works with environment to produce a phenotype – an organism’s physical appearance and performance.

But if those same genotypes grow in another field where the soil is poor, rain scarce or the farmer lazy, “not only will yields be lower – the ranking may change,” Schnable said. The best in the good environment may be worst in a low-input, high-stress environment.

“Understanding that is a fascinating biological question,” Schnable said, and vital to predicting which plant variety will produce which phenotype – grain yield and other characteristics – in a particular environment.

Schnable sees a chance for PSI and ISU to gain an international reputation by making some of the first major phenomic discoveries. As he told faculty, staff and students in a February lecture, “The goal is to understand the effects of genotype and the environment on phenotypes – or traits – sufficiently well that we will be able to predict phenotype given genotype and environment.”

In other words, Schnable aims to move PSI’s research from understanding and shaping a plant’s genes to create new, useful varieties, to using that information to actually predict how those variety will grow, appear, and produce food and fiber under specific environmental conditions.

Schnable sees at least three benefits from the project:

First and perhaps most obvious, researchers will use data to recommend which hybrids or varieties farmers should plant in a particular year, given their soil, location, management practice, weather predictions, and other environmental factors.

Third, and perhaps most importantly, they’ll be able to predict before testing which hybrids or varieties will perform best, allowing field testing to focus on only the most promising candidates.

Researchers will create statistical models – essentially complex graphs and curves – based on data of how genotypes performed under different environmental conditions. Knowing how genotypes scored statistically under conditions like drought, researchers can focus on testing only the ones that did best.

But to build the statistical model, researchers need the statistics – data on how plants behave in different environments. The more data they have, the more confident scientists can be about the models and predictions.

That’s what makes phenomic research such a challenge. Genetic data are “straightforward and relatively easy to generate,” Schnable said. “The phenotypic data we need to build models are very, very complex.”

That includes not just yield, but appearance, the pattern and depth of roots, disease or drought tolerance. Then there’s information on the environment – not just weather, but soil type, planting density and other factors.

You can look at it as an equation: Genetics (genotype), plus the environment, plus the interaction of genotype and environment (G times E) equals phenotype – the plant’s physical characteristics and performance.

The last part, the interaction, “is what makes it really hard,” Schnable said in his lecture.

Scientists can gather those data in controlled environments, like greenhouses and high-tech growth chambers, but the results also don’t always translate well to the real world, Schnable said.

“We think our special advantage here at Iowa State is to do it in the field. That has particular challenges, but we think that’s going to be most relevant to agriculture.”

ISU’s advantage is lots of field space, but getting good data will involve tracking hundreds of thousands, if not millions, of plants, growing in hundreds of places and conditions. Researchers will have to partner across institutional boundaries and, perhaps, even recruit farmers to participate.

Then there’s capturing, handling and analyzing those data. The effort will require partnerships between plant scientists, engineers and computational scientists.

The zombie tractor (my description, not Schnable’s) job has to do with that first part – gathering information about thousands of plants in the field. Working with Lie Tang, an ISU agricultural and biosystems engineering professor, and Maria Salas Fernandez, an ISU agronomy professor and sorghum breeder, Schnable is testing the contraption as a way to assemble phenotype data.

In the first test last summer, the tractor photographed plants from about 1,000 different sorghum genotypes at intervals during the growing season. The research was cut short because of hail damage to the plants, but the team will try again this summer.

With photos of a few representative plants from every genotype, “we can associate differences within a DNA sequence with differences in phenotypes among the thousand lines, all grown in roughly the same environment,” Schnable said.

Of course, having test plots only in Iowa would limit the scope of phenotype data. That’s why Schnable is collaborating with Natalia DeLeon, a University of Wisconsin agronomy professor, on what they call the Genomes to Fields initiatives.

They’re collaborating with around two dozen companies fellow academic breeders who will grow specific corn varieties at sites around the Midwest this summer. Each will gather data on environment and the resulting plant characteristics – phenotypes – of each set of plants.

The pilot project has support from the Iowa Corn Promotion Board. If all goes well, the team will scale up to thousands of genotypes at hundreds of locations in 2015 and continue for five years.